Performance evaluation of adaptive neuro-fuzzy inference system, artificial neural network and response surface methodology in modeling biodiesel synthesis from palm kernel oil by transesterification

References

• Demirbaş A. Biodiesel from vegetable oils via transesterification in supercritical methanol. Energ Convers Manage. 2002;43:2349–2356.
   Web of Science ®Google Scholar
• Leung DY, Wu X, Leung M. A review on biodiesel production using catalyzed transesterification. Appl Energy. 2010;87:1083–1095.
   Web of Science ®Google Scholar
• Knothe G. Biodiesel derived from a model oil enriched in palmitoleic acid, macadamia nut oil. Energy Fuels. 2010;24:2098–2103.
   Web of Science ®Google Scholar
• Konwar LJ, Das R, Thakur AJ, et al. Biodiesel production from acid oils using sulfonated carbon catalyst derived from oil-cake waste. J Mol Catal A-Chem. 2014;388:167–176.
   Google Scholar
• Yuan X, Liu J, Zeng G, et al. Optimization of conversion of waste rapeseed oil with high FFA to biodiesel using response surface methodology. Renew Energy. 2008;33:1678–1684.
   Web of Science ®Google Scholar
• Gui MM, Lee K, Bhatia S. Feasibility of edible oil vs. non-edible oil vs. waste edible oil as biodiesel feedstock. Energy. 2008;33:1646–1653.
   Web of Science ®Google Scholar
• Myers RH, Montgomery DC, Anderson-Cook C. Response surface methodology: product and process optimization using designed experiments. New York: John Wiley & Sons; 2009.
   Google Scholar
• Chen M-J, Chen K-N, Lin C-W. Optimization on response surface models for the optimal manufacturing conditions of dairy tofu. J Food Eng. 2005;68:471–480.
   Web of Science ®Google Scholar
• Sarve A, Sonawane SS, Varma MN. Ultrasound assisted biodiesel production from sesame (Sesamum indicum L.) oil using barium hydroxide as a heterogeneous catalyst: Comparative assessment of prediction abilities between response surface methodology (RSM) and artificial neural network (ANN). Ultrason Sonochem. 2015;26:218–228.
   PubMed Web of Science ®Google Scholar
• Betiku E, Akintunde AM, Ojumu TV. Banana peels as a biobase catalyst for fatty acid methyl esters production using Napoleon's plume (Bauhinia monandra) seed oil: A process parameters optimization study. Energy. 2016;103:797–806.
   Web of Science ®Google Scholar
• Betiku E, Okunsolawo SS, Ajala SO, et al. Performance evaluation of artificial neural network coupled with generic algorithm and response surface methodology in modeling and optimization of biodiesel production process parameters from shea tree (Vitellaria paradoxa) nut butter. Renew Energy. 2015;76:408–417.
   Web of Science ®Google Scholar
• Betiku E, Odude VO, Ishola NB, et al. Predictive capability evaluation of RSM, ANFIS and ANN: A case of reduction of high free fatty acid of palm kernel oil via esterification process. Energ Convers Manage. 2016;124:219–230.
   Web of Science ®Google Scholar
• Zobel CW, Cook DF. Evaluation of neural network variable influence measures for process control. Eng Appl Artif Intel. 2011;24:803–812.
   Web of Science ®Google Scholar
• Betiku E, Ajala SO. Modeling and optimization of Thevetia peruviana (yellow oleander) oil biodiesel synthesis via Musa paradisiacal (plantain) peels as heterogeneous base catalyst: A case of artificial neural network vs. response surface methodology. Ind Crops Prods. 2014;53:314–322.
   Web of Science ®Google Scholar
• Avramović JM, Veličković AV, Stamenković OS, et al. Optimization of sunflower oil ethanolysis catalyzed by calcium oxide: RSM versus ANN-GA. Energ Convers Manage. 2015;105:1149–1156.
   Web of Science ®Google Scholar
• Yaghoobi A, Bakhshi-Jooybari M, Gorji A, et al. Application of adaptive neuro fuzzy inference system and genetic algorithm for pressure path optimization in sheet hydroforming process. Int J Adv Manuf Tech. 2016;86:2667–2677.
   Web of Science ®Google Scholar
• Malik Z, Rashid K. Comparison of optimization by response surface methodology with neurofuzzy methods. Magnetics, IEEE Trans On. 2000;36:241–257.
   Web of Science ®Google Scholar
• Sajjadi B, Raman AAA, Parthasarathy R, et al. Sensitivity analysis of catalyzed-transesterification as a renewable and sustainable energy production system by adaptive neuro-fuzzy methodology. J Taiwan Inst Chem E. 2016;64:47–58.
   Web of Science ®Google Scholar
• Mostafaei M, Javadikia H, Naderloo L. Modeling the effects of ultrasound power and reactor dimension on the biodiesel production yield: Comparison of prediction abilities between response surface methodology (RSM) and adaptive neuro-fuzzy inference system (ANFIS). Energy. 2016;115:626–636.
   Web of Science ®Google Scholar
• Ighose BO, Adeleke IA, Damos M, et al. Optimization of biodiesel production from Thevetia peruviana seed oil by adaptive neuro-fuzzy inference system coupled with genetic algorithm and response surface methodology. Energ Convers Manage. 2017;132:231–240.
   Web of Science ®Google Scholar
• Dastorani MT, Moghadamnia A, Piri J, et al. Application of ANN and ANFIS models for reconstructing missing flow data. Environ Monit Assess. 2010;166:421–434.
   PubMed Web of Science ®Google Scholar
• Karimi S, Kisi O, Shiri J, et al. Neuro-fuzzy and neural network techniques for forecasting sea level in Darwin Harbor, Australia. Comput Geosci. 2013;52:50–59.
   Web of Science ®Google Scholar
• Kiran TR, Rajput S. An effectiveness model for an indirect evaporative cooling (IEC) system: Comparison of artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS) and fuzzy inference system (FIS) approach. Appl Soft Compt. 2011;11:3525–3533.
   Web of Science ®Google Scholar
• Taheri M, Moghaddam MA, Arami M. Techno-economical optimization of Reactive Blue 19 removal by combined electrocoagulation/coagulation process through MOPSO using RSM and ANFIS models. J Environ Manage. 2013;128:798–806.
   PubMed Web of Science ®Google Scholar
• Aminian M, Teimouri R. Application of soft computing techniques for modeling and analysis of MRR and taper in laser machining process as well as weld strength and weld width in laser welding process. Soft Comput. 2015;19:793–810.
   Web of Science ®Google Scholar
• Singhal R, Seth P, Bangwal D, et al. Optimization of biodiesel production by response surface methodology and genetic algorithm. J ASTM Int. 2012;9:1–7.
   Google Scholar
• Rajendra M, Jena PC, Raheman H. Prediction of optimized pretreatment process parameters for biodiesel production using ANN and GA. Fuel. 2009;88:868–875.
   Web of Science ®Google Scholar
• Naik M, Meher L, Naik S, et al. Production of biodiesel from high free fatty acid Karanja (Pongamia pinnata) oil. Biomass Bioenergy. 2008;32:354–357.
   Web of Science ®Google Scholar
• Betiku E, Adepoju TF. Methanolysis optimization of sesame (Sesamum indicum) oil to biodiesel and fuel quality characterization. IJEEE. 2013;4:1–8.
   Google Scholar
• Demirbaş A. Fuel properties and calculation of higher heating values of vegetable oils. Fuel. 1998;77:1117–1120.
   Web of Science ®Google Scholar
• Haldar S, Ghosh B, Nag A. Utilization of unattended Putranjiva roxburghii non-edible oil as fuel in diesel engine. Renew Energy. 2009;34:343–347.
   Web of Science ®Google Scholar
• Ma F, Hanna MA. Biodiesel production: a review. Bioresour Technol. 1999;70:1–15.
   Web of Science ®Google Scholar
• Betiku E, Omilakin OR, Ajala SO, et al. Mathematical modeling and process parameters optimization studies by artificial neural network and response surface methodology: A case of non-edible neem (Azadirachta indica) seed oil biodiesel synthesis. Energy. 2014;72:266–273.
   Web of Science ®Google Scholar
• Draper NR, Herzberg AM. On lack of fit. Technometrics. 1971;13:231–241.
   Web of Science ®Google Scholar
• Jang J-SR. ANFIS: adaptive-network-based fuzzy inference system. Syst, Man Cybern, IEEE Trans On. 1993;23:665–685.
   Web of Science ®Google Scholar
• Benjapornkulaphong S, Ngamcharussrivichai C, Bunyakiat K. Al 2 O 3-supported alkali and alkali earth metal oxides for transesterification of palm kernel oil and coconut oil. Chem Eng J. 2009;145:468–474.
   Web of Science ®Google Scholar
• Alamu O, Waheed M, Jekayinfa S. Biodiesel production from Nigerian palm kernel oil: effect of KOH concentration on yield. Energy Sustain Dev. 2007;11:77–82.
   Google Scholar
• Demirbas A. Biodiesel production via non-catalytic SCF method and biodiesel fuel characteristics. Energ Convers Manage. 2006;47:2271–2282.
   Web of Science ®Google Scholar
• Knothe G. Structure indices in FA chemistry. How relevant is the iodine value ? J Am Oil Chem Soc. 2002;79:847–854.
   Web of Science ®Google Scholar
• Joglekar A, May A. Product excellence through design of experiments. Cereal Foods World. 1987;32:857–868.
   Web of Science ®Google Scholar
• Dawodu FA, Ayodele O, Xin J, et al. Effective conversion of non-edible oil with high free fatty acid into biodiesel by sulphonated carbon catalyst. Appl Energy. 2014;114:819–826.
   Web of Science ®Google Scholar
• Nandi S, Mukherjee P, Tambe S, et al. Reaction modeling and optimization using neural networks and genetic algorithms: case study involving TS-1-catalyzed hydroxylation of benzene. Ind Eng Chem Res. 2002;41:2159–2169.
   Web of Science ®Google Scholar
• Kumar V, Kothiyal NC. Studies on carcinogenic PAHs emission generated by vehicles and its correlation to fuel and engine types. Polish J Chem Technol. 2014;16:48–58.
   Web of Science ®Google Scholar
• Gohain M, Devi A, Deka D. Musa balbisiana Colla peel as highly effective renewable heterogeneous base catalyst for biodiesel production. Ind Crops Prods. 2017;109:8–18.
   Web of Science ®Google Scholar
• Marchitan N, Cojocaru C, Mereuta A, et al. Modeling and optimization of tartaric acid reactive extraction from aqueous solutions: A comparison between response surface methodology and artificial neural network. Sep Purif Technol. 2010;75:273–285.
   Web of Science ®Google Scholar
• Simić VM, Rajković KM, Stojičević SS, et al. Optimization of microwave-assisted extraction of total polyphenolic compounds from chokeberries by response surface methodology and artificial neural network. Sep Purif Technol. 2016;160:89–97.
   Web of Science ®Google Scholar